docstr: Given examples and logits for those examples,
  compute the binary log odds for each example
docstr_tok: ["Given","examples","and","logits","for","those","examples",",","compute","the","binary","log","odds","for","each","example"]
code: def _logodds_from_logits(
      self, examples: list[LogoddsExample], logits: torch.Tensor
  ) -> list[float]:
      """Given examples and logits for those examples,
      compute the binary log odds for each example"""
      logodds_list = []
      for i, example in enumerate(examples):
          relevant_logits = self._extract_relevant_logits(
              logits, example, i
          )
          logprobs = F.log_softmax(relevant_logits, dim=-1)
          # NOTE: assuming always binary
          if len(logprobs) != 2:
              raise ValueError(
                  f"Expected len(logprobs) == 2, not {len(logprobs)}"
              )
          logodds = logprobs[0] - logprobs[1]
          logodds_list.append(logodds.item())
      return logodds_list
  
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from typing import Union, cast, Sequence
  from typing_extensions import Literal, get_args
  import numpy
  import torch
  import torch.nn.functional
  from transformers import AutoModelForCausalLM, AutoTokenizer
  from accelerate import Accelerator
  from .isp_dataset import ClassificationExample, Example, ExampleWithClasses, LogoddsExample, SequenceProbExample, NumericExample, TaskType
  from .isp_utils import BasicParser
  from .isp_utils import OpenAIModel, OpenaAIModelList
label_desc: Use image node_id 9 for calling the HFModel obj's underlying member method code with example usage: obj._logodds_from_logits(examples, logits) and returns: logodds_list